Prudvi Sai Padidala

Highly motivated and detail-oriented Data Science enthusiast with a strong foundation in computer science and hands-on experience in data analysis, machine learning, and programming (Python, C, SQL). Passionate about using data to improve business performance and customer experience. Skilled at leveraging data to develop actionable solutions to business challenges, utilizing data mining, and data visualization to create meaningful insights. Currently working as a Data Analyst intern at Foundever, refining technical skills and collaborating on data-driven solutions. Excels at transforming complex data into actionable results and eager to pursue advanced studies to further contribute to the field.

MongoDb (Mongodb course providing various certificates in CRUD Operations, Atlas search, Data modeling, etc.)

• Professional training program on Machine learning (Jan 2024)

An intensive training program in machine learning, covering essential concepts, algorithms, and applications conducted by "Cognibot". Gained hands-on experience with popular libraries such as TensorFlow and Scikit-learn, and developed proficiency in model building, evaluation, and deployment. Enhanced skills in data preprocessing, feature engineering, and statistical analysis, preparing for real-world data challenges. Also worked on the project Fake News Detection in this course.

• Bootcamp training on SQL and Python (Jul 2023)

 Completed a rigorous boot camp focused on SQL and Python for data analysis and manipulation conducted by "IMARTICUS LEARNING". Developed strong skills in querying databases, data cleaning, and data visualization. Gained handson experience with key libraries such as Pandas and NumPy, enabling effective data-driven decision-making. Prepared to apply these skills in real-world scenarios to solve complex data challenges. Also worked on the mini project in Diabetes prediction in this course.

• Fake News Detection Using Machine Learning Algorithms (Jan 2024 - Apr 2024)

To predict if the news article from the data pool is real or fake by using measures like TF(Term Frequency) and IDF(Inverse Document Frequency). It presents the analysis using various classifiers like Passive aggressive Classifier, Naive Bayes, Random forest, SVM, Logistic regression, Decision tree Classifier, and Stochastic Gradient Descent. The model providing the highest accuracy for the Testing data is implemented.

• Diabetes Prediction Using Machine Learning (Jul 2023 - Sep 2023)

 To predict if the individual has early stage Diabetes using a pool of input data and predict using Machine learning algorithms like K nearest neighbor, Logistic Regression, Random Forest, and SVM. It then chooses the model which provides the highest accuracy by using the Testing data

• Privacy-Preservation for Sensitive Data in Ecommerce using Reinforcement Learning Techniques (Jul 2024 - Present)

 It is to enhance E-commerce operations and also to protect privacy by integrating RL techniques, Q-learning, and PPO to maximize the results while adhering to proximal constraints. Homomorphic encryption is used to ensure sensitive data is protected. This project is currently under further development and also a Research paper supporting this project is being published in IEEE journal.